/*
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of
 * CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

#include <climits>

#include "ascir/Dialect/Asc/IR/Asc.h"
#include "ascir/Dialect/Asc/Transforms/Passes.h"
#include "ascir/Dialect/Utils/ConstantOpBuilder.h"

#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Value.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"

namespace mlir {
namespace ascendc {
#define GEN_PASS_DEF_MATERIALIZETENSOR
#include "ascir/Dialect/Asc/Transforms/Passes.h.inc"
} // namespace ascendc
} // namespace mlir

using namespace mlir;

namespace {

struct MaterializeLocalTensor : OpRewritePattern<ascendc::LocalTensorAutoOp> {
  using OpRewritePattern::OpRewritePattern;

  static ascendc::TPosition getPosition(ascendc::LocalTensorAutoOp op) {
    if (op.getOutput())
        return ascendc::TPosition::VECOUT;
    if (op.getInput())
        return ascendc::TPosition::VECIN;
    llvm_unreachable("position is undefined because tensor cannot be enqueued");
  }

  LogicalResult matchAndRewrite(ascendc::LocalTensorAutoOp op,
                                PatternRewriter &rewriter) const override {
    auto type = op.getType();
    auto loc = op.getLoc();
    ascir::ConstantOpBuilder consts(rewriter);
    Value length;
    if (type.hasStaticShape()) {
        length = consts.i64(type.getNumElements() * type.getElementTypeBitWidth() / CHAR_BIT);
    } else {
        assert(op->getNumOperands() != 0 && "must have operands for dynamic shape");
        length = consts.i64(type.getElementTypeBitWidth() / CHAR_BIT);
        for (auto dim : op.getDynamicShape()) {
            length = rewriter.create<arith::MulIOp>(loc, length, dim);
        }
    }
    Value pipe = rewriter.create<ascendc::PipeOp>(loc);
    if (!op.getInput() && !op.getOutput()) {
        auto bufferTy = ascendc::TBufType::get(op.getContext(), ascendc::TPosition::VECCALC);
        Value buffer = rewriter.create<ascendc::TBufOp>(loc, bufferTy);
        rewriter.create<ascendc::TPipeInitBufferOp>(loc, pipe, buffer, length);
        rewriter.replaceOpWithNewOp<ascendc::TBufGetTensorOp>(op, type, buffer);
        return success();
    }
    auto queueTy = ascendc::QueueType::get(op.getContext(), getPosition(op), 1);
    Value queue = rewriter.create<ascendc::QueueOp>(loc, queueTy);
    Value num = consts.i32(1);
    rewriter.create<ascendc::TPipeInitQueueOp>(loc, pipe, queue, num, length);
    auto allocOp =
        rewriter.replaceOpWithNewOp<ascendc::TQueBindAllocTensorOp>(op, type, queue);
    rewriter.setInsertionPoint(allocOp->getBlock()->getTerminator());
    rewriter.create<ascendc::TQueBindFreeTensorOp>(allocOp->getLoc(), queue, allocOp.getTensor());
    return success();
  }
};

class MaterializeTensorPass
    : public ascendc::impl::MaterializeTensorBase<MaterializeTensorPass> {
  void runOnOperation() override {
    func::FuncOp funcOp = getOperation();
    if (funcOp.isDeclaration()) {
      return;
    }
    MLIRContext *context = &getContext();
    RewritePatternSet patterns(context);
    patterns.add<MaterializeLocalTensor>(context);
    if (applyPatternsAndFoldGreedily(funcOp, std::move(patterns)).failed()) {
      signalPassFailure();
    }
  }
};
} // namespace

namespace mlir {
namespace ascendc {
std::unique_ptr<Pass> createMaterializeTensorPass() {
  return std::make_unique<MaterializeTensorPass>();
}
} // namespace ascendc
} // namespace mlir
